Abstract: The growth of demand response programs and renewable generation is changing the economics of transmission. Planners and regulators require tools to address the implications of possible technology, policy, and economic developments for the optimal configuration of transmission grids. We propose a model for economic evaluation and optimization of inter-regional transmission expansion, as well as the optimal response of generators' investments to locational incentives, that accounts for Kirchhoff's laws and three important nonlinearities. The first is consumer response to energy prices, modeled using elastic demand functions. The second is resistance losses. The third is the product of line susceptance and flows in the linearized DC load flow model. We develop a practical method combining Successive Linear Programming with Gauss-Seidel iteration to co-optimize AC and DC transmission and generation capacities in a linearized DC network while considering hundreds of hourly realizations of renewable supply and load. We test our approach for a European electricity market model including 33 countries. The examples indicate that demand response can be a valuable resource that can significantly affect the economics, location, and amounts of transmission and generation investments. Further, representing losses and Kirchhoff's laws is also important in transmission policy analyses.

Abstract: A low-complexity decision model for a Home Energy Management System is proposed to follow demand trajectory sets received from a Demand Side Response aggregator. This model is designed to reduce its computational complexity and being solved by low performance processors using available Single-Board Computers as a proof of concept. To decrease the computational complexity is proposed a two-stage model, where the first stage evaluates the hourly appliance scheduling using a relaxed set of restrictions, and the second stage evaluates a reduced set of appliances in a intra-hourly interval with a detailed characterization of the scheduled appliance properties. Simulations results show the effectiveness of the proposed algorithm to follow trajectories for different sets of home appliances and operational conditions. For the studied cases, the model presents deviations in the demand for the 3.2% of the cases in the first-stage and a 12% for the second-stage model. Results show that the proposed model can schedule available appliances according to the demand aggregator requirements in a limited solving time with diverse hardware.